In Table REF , we report a sub-table for each competitor, measuring the percentage of pixels with both available LiDAR and ground-truth values that are discarded, as well as the filtered RMSE. The RMSE without filtering is 2.5698 meters. Since the four competitors rely on a binary criterion to remove outliers, we both commit to i) remove the same amount of pixels they do and prove that our framework better reduces the error, ii) reduce the RMSE to the same value as our competitors and prove that our framework can achieve such an error by removing fewer points.
Thus, in each comparison, we respectively i) remove the same percentage of the competitor and measure our final RMSE (first column), ii) filter pixels as long as we get the same RMSE of the competitor and measure the % of pixels we removed to obtain it (second column).
Our method consistently achieves a much lower error when filtering the same percentage of pixels as the competitors. Moreover, it can reach the same final RMSE by removing a fraction of points, i.e. about 7-8 times less compared to stereo methods [1]}, [2]} yet not requiring two cameras as they do. Moreover, by committing to a single fixed threshold as one would do in a real application – e.g., by constantly removing only 5% pixels – our model outperforms all the competitors, with 0.5850 RMSE. Finally, we can notice how leveraging stereo matching generally removes a high percentage of points (20-30%) because of the several regions where stereo methods struggle, such as occlusions or untextured regions. In contrast, Surface [3]} removes very few points but yields a significantly higher RMSE.
